def cal_score(self):
distance = self.__how_far_are_agents(
) #which will be greater than or equal to zero.
if distance:
negotiations = SYSTEM.get_total_negotiation(
self.agent1.agent_id, self.agent2.agent_id)
part_a = (negotiations[1] - negotiations[2]) / float(
negotiations[0])
part_b = SYSTEM.get_fraction_change_in_price(self.entity.name)
part_b *= -1 if self.type_of_action == 'BUY' else part_b
part_c = 0
entity_global_avg_price = SYSTEM.get_entity_global_average_price(
self.entity.name)
if self.type_of_action == 'BUY':
buying_amount = self.agent1.get_entity_buying_amount(
self.entity.name)
if buying_amount != None and entity_global_avg_price != None and buying_amount != 0:
part_c = (buying_amount -
entity_global_avg_price) / float(buying_amount)
else:
selling_amount = self.agent1.get_entity_selling_amount(
self.entity.name)
if selling_amount != None and entity_global_avg_price != None and selling_amount != 0:
part_c = (entity_global_avg_price -
selling_amount) / float(selling_amount)
self.score = (self.part_0 + part_a + part_b +
part_c) / float(distance)
self.part_a = part_a
self.part_b = part_b
self.part_c = part_c
self.md = float(distance)
def make_csv(self, all_agents, sort_by):
from main import SYSTEM
df = pd.DataFrame(columns=[
'elasticity', 'patience', 'money', 'Total Negotiations',
'Total Positive', 'Total Negative', 'Entities Value Start',
'Entities Value End', 'Total Final Value', 'Total Starting Value',
'Earnings'
])
for agent in enumerate(all_agents):
negotiation_param = SYSTEM.get_negotiations_parameter_of_agent(
agent[1].agent_id)
entities_value_in_start = agent[1].cal_entities_value_in_start()
entities_of_agent = agent[1].entities_info.items()
total_quantity_price = 0
for entity_name, _ in entities_of_agent:
entity_info = agent[1].entities_info[entity_name]
if entity_info['isInterested']:
gap = SYSTEM.get_entity_global_average_price(entity_name)
if gap is None:
gap = (entity_info['min_selling_price'] +
entity_info['max_buying_price']) / 2
total_quantity_price += gap * entity_info['quantity']
val_start = constants.MONEY + entities_value_in_start
val_end = agent[1].money + total_quantity_price
df.loc[agent[0]] = [
agent[1].elasticity, agent[1].patience, agent[1].money,
negotiation_param[0], negotiation_param[1],
negotiation_param[2], entities_value_in_start,
total_quantity_price, val_end, val_start, val_end - val_start
]
df = df.sort_values(sort_by)
print(df)
df.to_csv('results/' + str(datetime.datetime.now()) + ".csv")